how to loop through a list of variable names to use it with the update function from lm

Question

I have a list of variable names that I would like to sequentially exclude from a best fitted model using the function update from lm. Because the list of variables are likely to change I want to loop through a given list but I can not get the elements of the list to be read as dependent variable. I found some code that I thought it could work:

Example code

hsb2 <-read.csv("www.ats.ucla.edu/stat/data/hsb2.csv")
names(hsb2)
varlist <- names(hsb2)[8:11]
models <- lapply(varlist, function(x) {
lm(substitute(read ~ i, list(i = as.name(x))), data = hsb2)
})

But not if I use the update function on a previous lm object

words<-c('Age','Sex', 'Residuals')
models <- lapply(words, function(x){update(substitute(
 lmobject,~.-i,list(i = as.name(x))),data =data_complete)})

I also tried

re<-c()
for (i in 1:3) {
lmt<-update(lmobject,~.-words[i])
r2no_i<-summary(lmt)$r.squared
re<-c(re, r2no_i)
}

I think this is pretty simple but I could not make the variable to be read properly Any tip is highly appreciated

Thanks for reply. lmobject comes from using the stepAIC from MASS package (lm function). lmobject is the object storing the final model chosen using backward method. Now I want to look at the contribution of some variables to the final model. That's when I got stuck. I do not want to rewrite a formula but to use update function to remove the variable of interest from the final model. — Laura, Mar 09 '16 at 15:33
well, then please make sure your example is [reproducible](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) such that all variables are defined. This makes it easier to help you. You can use `update(y~a+b+c+d, ~.-b)` to remove variables from a model but you need that second parameter to be a formula. Something like: `update(y~a+b+c+d, paste0("~.-", "b"))` — MrFlick, Mar 09 '16 at 15:39

score 0 · Answer 1 · answered Mar 09 '16 at 18:55

Is it possible that the built-in stats::drop1() function would do what you need?

Read data (note "http://..." is needed)

hsb2 <- read.csv("http://www.ats.ucla.edu/stat/data/hsb2.csv")
varlist <- names(hsb2)[8:11]

Fit all models: construct list of formulas:

formList <- lapply(varlist,reformulate,response="read")

Construct models (a little bit of fancy footwork is needed to get the $call component to look right)

modList <- lapply(formList,
                  function(x) {
                    m <- lm(x,data=hsb2)
                    m$call$formula <- eval(m$call$formula)
                    return(m)
})

words <- c('Age','Sex', 'Residuals')

pick one of the fitted models:

lmobject <- modList[[1]]

construct formulas of the form . ~ . - w

minus_form <- function(w) 
    reformulate(c(".",paste0("-",w)),response=".")

minus_form("abc")  ## . ~ . + -abc

Refit models with dropped terms:

newMods <- lapply(words,
                  function(w) {
                     update(lmobject,minus_form(w))
              })

how to loop through a list of variable names to use it with the update function from lm

Example code

1 Answers1